A Sociological Analysis of Structural Racism in Student List Products
ozanj.github.io/student_list_hsls/slides/student_list.html
Introduction
The market for college access
A two-sided matching problem in which market allocates students to colleges (Hoxby, 1997; Hoxby, 2009)
- Barriers to efficient market:
- transportation costs; information costs
- Students
- Goal: want to attend college
- Information problem: Don’t know where they will be admitted, how much it will cost
- Colleges want to enroll students
- Enrollment goals: academic profile; revenue; diversity; internal constituents
- Information problem: Don’t know who/where the “good” students are, how to contact them
Matchmaking
- “Inquiries” (student as first contact)
- Students send test scores or fill out inquiry form
- The enrollment problem
- Most colleges cannot survive/thrive solely from students who reach out on their own
- Must find desirable prospects who can be convinced to apply/enroll
Student lists products
“Student list” products are a matchmaking intermediary that connects colleges to prospects
- Third-party vendors obtain data and contact info about prospecs (e.g., Testing orgs, search engines)
- Vendors sell contact information of prospects to colleges looking for students
- Colleges choose prospect profiles by filtering on search filters (e.g., zip code, test score)
Policy concerns about student list products
- Problem with underlying products
- Search filters incorporate “racialized inputs” (Norris, 2021) that systematically disadvantage underrepresented students of color
- Problem with utilization of products
- University administrators may choose combinations of search filters that result in racial exclusion
Research questions
- What is relationship between search filters and racial composition of included vs excluded students?
- How do public universities use racialized search filters in concert with other search filters when purchasing student lists?
- What is observed racial composition of student list purchases that utilize racialized search filters in concert with other search filters?
Research overview
Research questions
- What is relationship between search filters and racial composition of included vs excluded students?
- Reconstruct College Board student list product using nationally representative sample of 9th graders from 2009 (NCES High School Longitudinal Survey)
- Simulate which students are included versus excluded when certain search filters are utilized
- How do public universities use racialized search filters in concert with other search filters when purchasing student lists?
- Analyze 830 student lists purchased by 14 public universities, collected via public records requests
- What is observed racial composition of student list purchases that utilize racialized search filters in concert with other search filters?
- Analyze targeted student list purchases, where we obtained both the order details and deidentified prospect-level data
Background & Literature
How student lists fit into recruiting undergraduates
Prospects
- Population of desirable potential students
Leads
- Prospects with contact info
Inquiries
- Prospects who have contacted the institution
- Institution as first contact (leads)
- Student as first contact
Interventions along the funnel
- Convert prospects to leads
- purchase student lists
- list-based lead generation
- based on direct mail model
- purchase student lists
- Convert leads/inquiries to applicants
- Email, mail, targeted social media
- Convert admits to enrolles
- Financial aid packages
Scholarship on recruiting from sociology
Enrollment funnel: prospects >> leads >> inquires >> applicants >> admits >> enrolled
- Most scholarship on enrollment management focuses on latter stages (admissions, financial aid)
- Body of research in sociology that analyzes recruiting “in the wild”
Recruiting from perspective of high school students (Holland, 2019)
- Underrepresented students sensitive to feeling “wanted” by colleges
Connections between high schools and colleges from organizational behavior perspective
- Off-campus recruiting visits indicate a network tie and enrollment priorities
- recruiting from perspective of private college (Stevens, 2007), private HS counselors (Khan, 2011)
- Recruiting visits by public research univs (e.g., Salazar, Jaquette, and Han, 2021; and Salazar, 2022)
Recruiting at open-access PSIs for adults (e.g., and Cottom, 2017; and Posecznick, 2017)
- For-profits have demand in Black/Latinx communities because traditional colleges ignore them
Scholarship assumes that recruiting is something done wholly done by individual colleges
- Third-party products and vendors structure recruiting behavior by colleges
- How do products incorporate structural inequality? How are they utilized by colleges?
College Board and ACT student list products
Sources of student list data
- Test takers (e.g., PSAT, SAT, AP), pre-test questionnaire (demographics, preferences)
- More recently, from college search engines (e.g., College Board Big Future)
- Students can opt in or out
What information does a list contain (College Board template)
- Contact, demographic, college preferences, limited academic achievement
Pricing
- Historically, price-per-prospect (e.g., $0.50 per name); now a subscription model
Buying lists: “search filters” control which prospects included in purchase
- Commonly used search filters (Link to ACT filters)
- e.g., HS GPA, test score range, gender, race, geography (e.g., state), enrollment probability
- Salazar, Jaquette, and Han (2022) categorizes College Board search filters into four buckets:
- Geographic
- Academic
- Demographic
- Student preferences
Filters used in 830 lists purchased by 14 public universities
College Board lists and student outcomes
Howell, Hurwitz, Mabel et al. (2021)
also see: Smith, Howell, and Hurwitz (2022) for effect of university purchasing profile on college choice
Conceptual Framework
Sociology of race
Selection devices allocate individuals to categories based on input factors (Hirschman and Bosk, 2020)
- Standardized selection devices
- decisions based on mathematical function in which input values determine outcome value
- “actuarial” selection devices predict outcomes based on analysis of past cases
- Discretionary selection devices
- Decisions incorporate judgment of individual evaluators (e.g., holistic admissions)
- Student list products are discretionary selection devices
Standardized selection devices and racial inequality
- May reduce racial inequality if source of inequality is bias from individual decision-maker
- Do not reduce inequality due to structural racism (Bonilla-Silva, 1997)
- Structural racism is “systematic racial bias embedded in the ‘normal’ functions of laws and social relations,” whereby processes viewed as neutral or common-sense systematically disadvantage marginalized groups (Tiako, South, and Ray, 2021, p. 1143)
Discretionary selection devices and racial inequality
- Sensitive to individual bias from decision-makers and inputs related to race
Racialized inputs
Norris (2021)
- reconstructs inputs used by Moody’s city government credit rating algorithm
- Racialized inputs: “theoretically and empirically correlated with historical racial disadvantage” (p. 5)
- “colorblind” selection devices reproduce racial inequality by using seemingly neutral inputs that are systematically related to race because marginalized groups have been historically excluded from input
Geography inputs
- US racially segregated due to historic and current laws/policies/practices
- Zip code an input in algorithm that predicts crime recidivism (O’Neil, 2016; Benjamin, 2019)
- Zip code a search filter on CB/ACT student list products
- Ruffalo Noel Levitz algorithm recommends which zip codes to buy names
- Geodemographic classifications classify localities by consumer behavior (Leyshon and Thrift, 1999)
- CB Geodemographic filters classify census tracts by past college enrollment (College Board, 2011)
Inputs based on predictive analytics
- Analyze determinants of an outcome in past cases, use results as an input to select future cases (e.g., ACT “enrollment predictor” filter)
RQ1: relationship between filters and racial composition
Standardized college entrance exams and AP exams as racialized inputs
- Racial/ethnic differences in test-taking and test scores a function of historic/contemporary school segregation, differences in school funding, differences in access to curricula (e.g., Reardon, Kalogrides, and Shores, 2019; Rodriguez and Hernandez-Hamed, 2020)
P1: The condition of taking standardized assessments is associated with racial disparities in who is included versus excluded in student list products.
P2: As test score threshold increases, proportion of underrepresented minority students included in student lists declines relative to proportion who are excluded.
P3. As purchases filter on more affluent zip codes, the proportion of underrepresented minority students included in student lists declines relative to proportion excluded.
P4. Filtering on smaller geographic localities is associated with greater racial disparities in included vs. excluded than filtering on larger geographic localities.
Filtering on multiple racialized inputs has compounding effect on racial inequality
Methods
Data
HSLS09 & Student List Project
High School Longitudinal Study of 2009 (HSLS09)
- Nationally representative survey of 23,000 students from 944 schools entering 9th grade in Fall 2009
- Follow up surveys: Spring 2012 (11th grade), 2013 (HS graduation/transcripts), 2016 (postsecondary)
- Sample: Students who completed 2012/2013 survey follow-ups + HS transcripts
- Unweighted: N=16,530
- Weighted analysis sample: N=4.2 Million
Student List Project
- Issued public records requests for student list data (2016-2020) to all public universities in four states (CA, IL, MN, TX)
- Target student list vendors: College Board, ACT
- Data collected for each purchased list:
- Funded by Joyce Foundation and Kresge Foundation
Research design
Variables & Analyses
RQ1. What is relationship between search filters and racial composition of included vs excluded students?
- Data: HSLS09
- Dependent Variable: student race/ethnicity
- Independent Variables: measures of student list academic and geographic filters
- Dichotomous and score threshold measures of SAT, PSAT, AP (and GPA)
- Note: HSLS measures for SAT includes ACT, and PSAT includes PreACT
- Economic measures of students’ zipcode, county, CBSA from ACS
- Dichotomous and score threshold measures of SAT, PSAT, AP (and GPA)
- Analyses: inferential descriptive statistics (comparing two propoportions)
- Racial composition of included vs excluded prospects when particular filters utilized
RQ2. How do public universities use racialized search filters in concert with other search filters when purchasing student lists?
- Data: order summaries from 830 College Board student lists purchased by 14 public universities
- Analyses: descriptive statistics of search filters specified in orders
RQ3. What is observed racial composition of student list purchases that utilize racialized search filters in concert with other search filters?
- Data: de-identified prospect-level student list data; incorporate ACS data for comparisons
- Analyses: descriptive statistics of targeted student list purchases
Results
RQ1
Test Takers
P1: Racial disparities in test-taking
Test Thresholds
P2: SAT, PSAT score thresholds and racial composition
Test Thresholds
P2: AP score thresholds and racial composition
Geography
P3: Zip code affluence and racial composition, within CBSA
Geography
P4: Zip vs County, affluence percentiles within CBSA
Compounding Effects
GPA (3.0+) and SAT or PSAT (across score thresholds)
Compounding Effects
GPA (3.0+), PSAT (150+), SAT (1050+), and Zip (by income)
Compounding Effects
GPA (3.0+) and AP (across score thresholds)
RQ2
Broad patterns
Filters used in order purchases
Combination of filters
Filter combos used in order purchases
| Research | MA/doctoral | ||||
|---|---|---|---|---|---|
| Filters | Count | Percent | Filters | Count | Percent |
| HS grad class, GPA, SAT, PSAT, Rank, State, Race | 39 | 10% | HS grad class, GPA, SAT, Zip code | 206 | 45% |
| HS grad class, PSAT, State | 27 | 7% | HS grad class, GPA, PSAT, Zip code | 145 | 32% |
| HS grad class, GPA, PSAT, State, Race | 20 | 5% | HS grad class, SAT, State | 31 | 7% |
| HS grad class, PSAT, State, Low SES | 20 | 5% | HS grad class, GPA, SAT, PSAT, Zip code | 28 | 6% |
| HS grad class, GPA, PSAT, State | 17 | 5% | HS grad class, GPA, SAT, State | 7 | 2% |
| HS grad class, GPA, SAT, State | 16 | 4% | HS grad class, SAT, Geomarket | 6 | 1% |
| HS grad class, GPA, AP score, Geomarket | 15 | 4% | HS grad class, GPA, SAT, County | 5 | 1% |
| HS grad class, GPA, SAT, PSAT, State, Segment, Gender | 13 | 3% | HS grad class, GPA, SAT, PSAT, County | 4 | 1% |
| HS grad class, PSAT, Geomarket | 12 | 3% | HS grad class, GPA, PSAT, State | 2 | 0% |
| HS grad class, SAT, State, Low SES, College size | 11 | 3% | HS grad class, SAT, Geomarket, College type | 2 | 0% |
RQ3
Characteristics by filters
Prospect characteristics across individual filter criteria
| Academic | Geographic | Demographic | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| All domestic | GPA | PSAT | SAT | HS rank | AP score | Zip code | State | Geomarket | Segment | CBSA | Race | Gender | ||||
| Total | 3,547,620 | 1,101,266 | 1,812,447 | 971,237 | 146,660 | 75,479 | 165,924 | 1,173,678 | 1,056,951 | 186,519 | 146,313 | 279,626 | 39,546 | |||
| Location | ||||||||||||||||
| % In-state | 38 | 62 | 30 | 54 | 83 | 42 | 98 | 48 | 17 | 15 | 4 | 59 | 6 | |||
| % Out-of-state | 62 | 38 | 70 | 46 | 17 | 58 | 2 | 52 | 83 | 85 | 96 | 41 | 94 | |||
| Race/ethnicity | ||||||||||||||||
| % White | 48 | 45 | 50 | 47 | 51 | 17 | 43 | 42 | 57 | 51 | 53 | 25 | 47 | |||
| % Asian | 16 | 15 | 17 | 15 | 10 | 7 | 13 | 18 | 13 | 27 | 28 | 5 | 38 | |||
| % Black | 5 | 7 | 4 | 7 | 8 | 17 | 8 | 5 | 4 | 3 | 2 | 11 | 1 | |||
| % Latinx | 21 | 24 | 19 | 22 | 23 | 46 | 27 | 24 | 16 | 11 | 8 | 46 | 6 | |||
| % AI/AN | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 2 | 0 | |||
| % NH/PI | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |||
| % Multiracial | 5 | 5 | 5 | 5 | 5 | 10 | 4 | 6 | 5 | 5 | 5 | 9 | 5 | |||
| % Other | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |||
| % No response | 4 | 3 | 3 | 3 | 2 | 1 | 4 | 3 | 4 | 3 | 3 | 2 | 3 | |||
| % Missing | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | |||
| Gender | ||||||||||||||||
| % Male | 34 | 19 | 37 | 18 | 0 | 3 | 46 | 24 | 48 | 6 | 0 | 11 | 0 | |||
| % Female | 36 | 23 | 40 | 20 | 1 | 15 | 54 | 27 | 52 | 9 | 0 | 12 | 33 | |||
| % Other | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |||
| % Missing | 30 | 58 | 22 | 63 | 99 | 82 | 0 | 49 | 0 | 85 | 1 | 77 | 67 | |||
| Household income | ||||||||||||||||
| Median income | $107K | $105K | $108K | $105K | $99K | $90K | $97K | $105K | $107K | $130K | $135K | $94K | $127K | |||
| Locale | ||||||||||||||||
| % City | 27 | 27 | 27 | 26 | 26 | 31 | 31 | 30 | 23 | 24 | 22 | 29 | 26 | |||
| % Suburban | 44 | 47 | 44 | 48 | 53 | 40 | 42 | 42 | 46 | 54 | 57 | 47 | 49 | |||
| % Rural - Fringe | 22 | 20 | 22 | 20 | 15 | 23 | 19 | 22 | 23 | 19 | 19 | 19 | 23 | |||
| % Rural - Distant | 6 | 6 | 5 | 6 | 6 | 5 | 7 | 6 | 6 | 2 | 1 | 6 | 2 | |||
| % Rural - Remote | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | |||
| % Missing | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |||
Geodemographic segment filters
Filter by neighborhood segments
| 2011 D+ Cluster | SAT Math | SAT CR | Going Out of State | Percent NonWhite | Need Financial Aid | Med Income |
|---|---|---|---|---|---|---|
| 51 | 546.00 | 533.00 | 32% | 30% | 57% | $95,432 |
| 52 | 480.00 | 470.00 | 30% | 58% | 71% | $63,578 |
| 53 | 561.00 | 544.00 | 32% | 50% | 55% | $92,581 |
| 54 | 458.00 | 443.00 | 25% | 83% | 76% | $38,977 |
| 55 | 566.00 | 565.00 | 52% | 24% | 63% | $71,576 |
| 56 | 420.00 | 411.00 | 29% | 93% | 66% | $35,308 |
| 57 | 541.00 | 519.00 | 52% | 47% | 43% | $67,394 |
| 58 | 533.00 | 489.00 | 28% | 87% | 69% | $68,213 |
| 59 | 561.00 | 562.00 | 52% | 24% | 74% | $54,750 |
| 60 | 589.00 | 590.00 | 63% | 37% | 36% | $104,174 |
| 61 | 585.00 | 567.00 | 51% | 30% | 40% | $123,858 |
| 62 | 596.00 | 595.00 | 67% | 24% | 72% | $59,824 |
| 63 | 548.00 | 541.00 | 39% | 23% | 65% | $69,347 |
| 64 | 466.00 | 466.00 | 48% | 34% | 29% | $49,829 |
| 65 | 440.00 | 433.00 | 23% | 93% | 78% | $45,081 |
| 66 | 499.00 | 492.00 | 20% | 12% | 76% | $50,453 |
| 67 | 519.00 | 501.00 | 27% | 53% | 59% | $60,960 |
| 68 | 552.00 | 558.00 | 52% | 35% | 65% | $57,902 |
| 69 | 534.00 | 521.00 | 37% | 19% | 65% | $88,100 |
| 70 | 613.00 | 598.00 | 65% | 29% | 61% | $86,381 |
| 71 | 405.00 | 408.00 | 39% | 97% | 68% | $42,661 |
| 72 | 399.00 | 397.00 | 31% | 87% | 47% | $32,708 |
| 73 | 528.00 | 514.00 | 29% | 42% | 62% | $90,849 |
| 74 | 433.00 | 435.00 | 29% | 84% | 79% | $44,065 |
| 75 | 459.00 | 457.00 | 28% | 85% | 72% | $50,421 |
| 76 | 514.00 | 509.00 | 27% | 38% | 64% | $61,332 |
| 77 | 502.00 | 492.00 | 26% | 18% | 75% | $62,372 |
| 78 | 594.00 | 578.00 | 56% | 26% | 39% | $134,400 |
| 79 | 550.00 | 551.00 | 57% | 32% | 74% | $40,909 |
| 80 | 534.00 | 527.00 | 39% | 39% | 65% | $49,877 |
| 81 | 491.00 | 483.00 | 27% | 57% | 72% | $63,030 |
| 82 | 496.00 | 491.00 | 29% | 21% | 75% | $53,465 |
| 83 | 500.00 | 490.00 | 19% | 26% | 71% | $49,335 |
| Total | 512.00 | 502.00 | 32% | 43% | 65% | $70,231 |
Filter by high school segments
| 2011 D+ Cluster | SAT Math | SAT CR | Going Out of State | Percent NonWhite | Need Financial Aid | Med Income |
|---|---|---|---|---|---|---|
| 51 | 462.00 | 457.00 | 14% | 33% | 68% | $40,918 |
| 52 | 489.00 | 496.00 | 81% | 99% | 77% | $64,730 |
| 53 | 471.00 | 484.00 | 28% | 38% | 62% | $60,833 |
| 54 | 376.00 | 371.00 | 33% | 96% | 38% | $38,146 |
| 55 | 489.00 | 481.00 | 39% | 46% | 44% | $71,845 |
| 56 | 536.00 | 508.00 | 73% | 43% | 49% | $63,967 |
| 57 | 434.00 | 435.00 | 29% | 82% | 79% | $48,301 |
| 58 | 592.00 | 577.00 | 51% | 27% | 32% | $104,509 |
| 59 | 499.00 | 489.00 | 19% | 18% | 74% | $47,685 |
| 60 | 523.00 | 549.00 | 23% | 30% | 33% | $70,175 |
| 61 | 485.00 | 370.00 | 33% | 89% | 9% | $61,385 |
| 62 | 474.00 | 473.00 | 34% | 92% | 67% | $55,515 |
| 63 | 440.00 | 427.00 | 28% | 86% | 72% | $49,238 |
| 64 | 606.00 | 542.00 | 37% | 89% | 57% | $81,911 |
| 65 | 515.00 | 503.00 | 28% | 43% | 65% | $72,692 |
| 66 | 498.00 | 515.00 | 37% | 37% | 73% | $60,272 |
| 67 | 526.00 | 546.00 | 48% | 41% | 69% | $71,279 |
| 68 | 541.00 | 540.00 | 41% | 26% | 62% | $79,260 |
| 69 | 390.00 | 395.00 | 36% | 92% | 74% | $43,391 |
| 70 | 595.00 | 581.00 | 56% | 33% | 48% | $105,721 |
| 71 | 400.00 | 412.00 | 57% | 98% | 80% | $43,137 |
| 72 | 528.00 | 544.00 | 35% | 25% | 64% | $70,018 |
| 73 | 451.00 | 438.00 | 24% | 89% | 76% | $48,406 |
| 74 | 654.00 | 579.00 | 76% | 80% | 46% | $59,089 |
| 75 | 514.00 | 502.00 | 31% | 20% | 71% | $72,850 |
| 76 | 600.00 | 584.00 | 72% | 50% | 28% | $90,265 |
| 77 | 595.00 | 508.00 | 64% | 75% | 39% | $39,490 |
| 78 | 473.00 | 468.00 | 48% | 43% | 22% | $56,703 |
| 79 | 594.00 | 585.00 | 61% | 26% | 71% | $65,180 |
| Total | 514.00 | 502.00 | 32% | 44% | 65% | $70,223 |
Segment filter prospects by metro
Filters: HS Class, Segment, GPA (B-A+), PSAT/SAT (1220-1450); State/CBSAs
Segment filter prospects interactive map
“Basic tenet of geodemography is people with similar cultural backgrounds, means, and perspectives naturally gravitate to one another or form relatively homogeneous communities” (College Board, 2011)
Women in STEM
Women in STEM prospects by metro
Filters: HS Class, AP STEM (3 min for in-state; 4 min for out-of-state) or SAT (1200 minimum for in-state; 1300 minimum for out-of-state) with STEM major interest, GPA (B-A+), State (in-state vs. out-of-state)
Targeting URM students
Race and ethnicity variables, aggregated vs. alone
Filters: HS Class, SAT (1200-1380), GPA (B-A+), Race (Latinx, Black, AIAN), State
Purchased profiles for students of color by metro
Purchased profiles for students of color interactive map
Zip code & test score filters
Los Angeles prospects from top income decile zip codes
Filters: HS Class, PSAT (high/low) or SAT (high/low), Zip Code (by affluence)
Discussion
Future research on student list products
Improve this paper enough to get published
- How to motivate targeted analyses about how universities utilize student list products
Papers on College Board filters that create geographic borders from analyses of past college enrollment
- Geomarket filters slice metropolitan areas into several education markets
- Geodemography filters categorize neighborhoods and high schools into clusters
Explore link between student list products and extension of credit
- Parallel origins of consumer credit reports and student lists (Hoxby, 2009; Leyshon and Thrift, 1999)
- Reduce information assymetry by providing businesses information about customers
- Business model moves from evaluation of applicants to active courtship of desired customers
- Consumer credit reports regulated under Fair Credit Reporting Act and Consumer Finance Protection Act because they lead to extension of credit
- Student list products systematically lead to student loans
Regulatory policy
- Students list products are powerful tools, can be used for good or bad
- Should policymakers continue tolerating products likely to do harm because they also do good?
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Appendix
RQ1
Test Thresholds
GPA
RQ2
Data collection
Summary of data received
| State | # received order summary | # no order summary | # received list | # no list | # received both | # did not receive both |
|---|---|---|---|---|---|---|
| CA | 9 | 23 | 13 | 19 | 9 | 23 |
| IL | 9 | 3 | 9 | 3 | 8 | 4 |
| TX | 15 | 20 | 16 | 19 | 10 | 25 |
Orders and prospects purchased
Academic filters
GPA filter used
SAT filter used
PSAT filter used
Geographic filters
State filter used by research universities, out-of-state
State filter used by research universities, in-state
Demographic filters
Race filter
